【24h】

Detecting Thai Messages Leading to Deception on Facebook

机译:在脸书上检测导致欺骗的泰国信息

获取原文

摘要

Social network has become a very popular communication for Thai people, especially Facebook. Unfortunately, this popularity also attracts deceiver spreading malicious messages to other users. Some messages lead to deception. This paper studies Thai messages posted on Facebook that lead to deception. We try to investigate different approaches to detect deceptive messages and find dominant words. To detect deceptive messages, the dataset is retrieved from Facebook pages. Next, content-based and context-based features are extracted from the dataset. Two algorithms, i.e. SVM and KNN, are applied to perform a prediction. We construct the experiments to investigate context-based and content-based features for detecting deceptive messages. The experimental results show that the context-based features gives the best performance and the F-measure for predicting deceptive messages achieves 99 % when using SVM classifier. In addition, dominant words in deceptive messages and truthful messages are reported in our work.
机译:社交网络已成为泰国人(尤其是Facebook)非常流行的交流方式。不幸的是,这种流行也吸引了欺骗者将恶意消息传播给其他用户。一些消息会导致欺骗。本文研究了在Facebook上发布的导致欺骗的泰国信息。我们尝试研究不同的方法来检测欺骗性消息并找到优势词。为了检测欺骗性消息,从Facebook页面检索数据集。接下来,从数据集中提取基于内容和基于上下文的特征。应用两种算法,即SVM和KNN,来执行预测。我们构建实验以调查基于上下文和基于内容的功能,以检测欺骗性消息。实验结果表明,使用SVM分类器时,基于上下文的功能可提供最佳性能,并且F-measure预测欺骗性消息的效果达到了99%。此外,在我们的工作中还报道了欺骗性信息和真实信息中的主导词。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号